Electrical and Computer Engineering ETDs

Publication Date

Fall 11-10-2020


Artificial Intelligence (AI) based techniques are typically used to model decision-making in terms of strategies and mechanisms that can conclude to optimal payoffs for a number of interacting entities, often presenting competitive behaviors. In this thesis, an AI-enabled multi-access edge computing (MEC) framework is proposed, supported by computing-equipped Unmanned Aerial Vehicles (UAVs) to facilitate Internet of Things (IoT) applications. Initially, the problem of determining the IoT nodes optimal data offloading strategies to the UAV-mounted MEC servers, while accounting for the IoT nodes’ communication and computation overhead, is formulated based on a game-theoretic model. The existence of at least one Pure Nash Equilibrium (PNE) point is shown by proving that the game is submodular. Furthermore, different operation points (i.e., offloading strategies) are obtained and studied, based either on the outcome of Best Response Dynamics (BRD) algorithm, or via alternative reinforcement learning approaches, such as gradient ascent, log-linear and Q-learning algorithms, which explore and learn the environment towards determining the users’ stable data offloading strategies. The respective outcomes and inherent features of these approaches are critically compared against each other, via modeling and simulation.


Artificial Intelligence, Reinforcement Learning, Edge, IoT

Document Type




Degree Name

Computer Engineering

Level of Degree


Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Dr. Eirini Eleni Tsiropoulou

Second Committee Member

Dr. Marios Pattichis

Third Committee Member

Dr. Xiang Sun